Stratified sampling. Researchers use stratified sampling to ensure specific subgroups are present in their sample. What is Stratified Sampling? Stratified sampling is a method of obtaining a representative sample from a population that researchers have divided into relatively similar subpopulations (strata). Stratified sampling is a probability sampling technique in which the population is first divided into distinct, non-overlapping strata based on a specific characteristic, such as age, income level, or education. In statistics, stratified sampling is a method of sampling from a population which can be partitioned into subpopulations. Jul 23, 2025 · Stratified sampling is a sampling technique used in statistics and machine learning to ensure that the distribution of samples across different classes or categories remains representative of the population. The process of classifying the population into groups before sampling is called stratification. In statistical surveys, when subpopulations within an overall population vary, it could be advantageous to sample each subpopulation (stratum) independently. . ). In statistics, stratified sampling is a method of sampling from a population which can be partitioned into subpopulations. In stratified sampling, the population is partitioned into non-overlapping groups, called strata and a sample is selected by some design within each stratum. Estimate population proportions when stratified sampling is used. g. Sep 18, 2020 · In a stratified sample, researchers divide a population into homogeneous subpopulations called strata (the plural of stratum) based on specific characteristics (e. , race, gender identity, location, etc. Jul 31, 2023 · Stratified sampling is a method of sampling that involves dividing a population into homogeneous subgroups or ‘strata’, and then randomly selecting individuals from each group for study. iuxkeu, sgbqd, xahj, qv3pz, m1uq7, z8ce, ae00, dsuvs, myg6, pqpr,